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on Computational Economics |
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Issue of 2008‒06‒13
two papers chosen by |
| By: | Dániel Holló (Magyar Nemzeti Bank); Mónika Papp (Magyar Nemzeti Bank) |
| Abstract: | This paper investigates the main individual driving forces of Hungarian household credit risk and measures the shockabsorbing capacity of the banking system in relation to adverse macroeconomic events. The analysis relies on survey evidence gathered by the Magyar Nemzeti Bank (MNB) in January 2007. Our study presents three alternative ways of modelling household credit risk, namely the financial margin, the logit and the neural network approaches, and uses these methods for stress testing. Our results suggest that the main individual factors affecting household credit risk are disposable income, the income share of monthly debt servicing costs, the number of dependants and the employment status of the head of the household. The findings also indicate that the current state of indebtedness is unfavourable from a financial stability point of view, as a relatively high proportion of debt is concentrated in the group of risky households. However, risks are somewhat mitigated by the fact that a substantial part of risky debt is comprised of mortgage loans, which are able to provide considerable security for banks in the case of default. Finally, our findings reveal that the shock-absorbing capacity of the banking sector, as well as individual banks, is sufficient under the given loss rate (LGD) assumptions (i.e. the capital adequacy ratio would not fall below the current regulatory minimum of 8 per cent) even if the most extreme stress scenarios were to occur. |
| Keywords: | financing stability, financial margin, logit model, neural network, stress test. |
| JEL: | C45 D14 E47 G21 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:mnb:opaper:2008/70&r=cmp |
| By: | Marco Valente |
| Abstract: | LSD is one of many programming languages designed to develop agent-based models. LSD implements time-driven models expressed in formats equivalent to discrete systems of equations, where each equation computes the value of a generic instance of a variable at a generic time step. LSD models are therefore extremely parsimonious in terms of details that users must provide to the system. When a model has been described, the system automatically generates a working program implementing the model, endowed with a complete set of interfaces for any possible operation on the model. The major feature of is that users can rely on an automatic scheduling system and on automatic retrieval of data required for the equations. Such features are particularly attractive in complex, multi-herarchical models. They permit even non- expert programmers to develop even relatively complex models with minimal training. The systems interfaces guarantee the complete control of the model at building, at run-time and at post-simulation analysis, facilitating debugging, revisions and detailed analysis of model results, which are useful properties especially when developing large models for ambitious projects. The design of LSD is based on an "open architecture", so that LSD can be used to implement any type of model, including even-driven models and models based on customized data structures. The intrinsic modularity of LSD models make them easily scalable facilitating the development of highly complex models by demanding users. The underlining layer of C++, accessible by the users, allows the inclusions of external libraries or of complex data structures, besides an extreme speed and dimensions of the model. This work reports on the major features of the design of LSD outlining its most prominent advantages for users of simulation models in research, particularly for agent- based simulations. |
| Keywords: | Simulations models, programming languages |
| Date: | 2008–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2008/12&r=cmp |